One of the key aspects of building a good machine learning model is Feature engineering. Feature engineering is a process where we create new features from existing raw features. To create new features, we require domain experts who have knowledge of the subject. By using their knowledge they create new features which are helpful for a machine to learn better. The time taken by the domain experts to understand the data and then create new features is time-consuming and expensive. This problem is addressed with a neural network which will not require domain experts to engineer new features. Current paper deals with the case study pertaining to the data of Human Action Recognition. Using the data, the machine predicts the various physical actions and appearances of a person like if the person is sitting, standing, walking, walking up stairs, and walking downstairs or lying. We compare the accuracy of the model using data which was feature engineered by experts and the model which was not feature engineered by the domain experts.

Machine Learning, Feature Engineering, Domain Knowledge, Human Action Recognition, Neural Networks

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